Background:
Horseradish Peroxidase (HRP) is an important biocatalyst extensively used in enzymatic reactions. Cholesterol oxidase (ChoX) is a commercially valuable enzyme used in the estimation of cholesterol in human serum. ChoX is an oxygen oxidoreductase class of enzyme which catalyzes the oxidation of cholesterol in the presence of O2, liberating hydrogen peroxide H2O2 as a by-product. HRP catalyzes the reduction of this H2O2 in the presence of a redox dye (chromophore), producing a pink colored Quinoneimine which can be measured spectrophotometrically. The use of soluble HRP makes this assay method expensive for each time use and the recovery of HRP is not possible.
Objective:
Our aim was to prepare the HRP immobilized beads having magnetic properties for the ease of separation and increasing the reusability of HRP for the low cost ChoX assay.
Methods:
In the present work, we prepared magnetic chitosan beads using chitosan-Fe2O3 nanoparticle blend coated with Graphene Oxide (GO), and subsequently activated with 2.5% glutaraldehyde (GA). Enzyme loaded beads were characterized by SEM, FTIR, and XRD analysis.
Results:
The immobilization efficiency was ~80% and the immobilized HRP retained 90% of its initial activity up to 12 times reuse. The pH and temperature optima were shifted from 6.5 and 50°C for soluble HRP to 7.0 and 55°C for the immobilized HRP, respectively. Storage stability of immobilized HRP was 93.72% and 60.97% after 30 and 60 days storage respectively, at 4°C.
Conclusion:
On the basis of the present study, the HRP loaded magnetic chitosan/graphene oxide beads could be used for low-cost ChoX assay at laboratory scale due to its enhanced reusability and stability.
Cholesterol oxidase (COX) is widely used enzyme for total cholesterol estimation in human serum and for the fabrication of electro-chemical biosensors. COX is also used for the bioconversion of cholesterol; for the production of precursors of steroidal drugs and hormones. Enzyme activity depends decisively on defined conditions with respect to pH, temperature, ionic strength of the buffer, substrate concentration, enzyme concentration, reaction time. Standardization of these parameters is desirable to attain optimum activity of the enzyme. The present work aims to build a neural network model using five input parameters (pH, cholesterol concentration, 4-aminoantipyrine concentration, crude COX volume and horseradish peroxidase) and one output i.e., COX activity (U/ml) as a response. A feed forward back propagation neural network with Levenberg-Marquardt training algorithm was used to train the network. The network performance was assessed in terms of regression (2 R), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). A network topology of 5-10-1 was found to be optimum. The MSE, MAPE and 2 R values of the neural model were 0.0075%, 0.12% and 0.9792% respectively. The maximum predicted activity of COX was 1.073 U/ml, which was close to the experimental value i.e., 1.1 U/ml at simulated optimum assay conditions. MSE and MAPE depicted the precision in the prediction efficiency of the developed ANN model. Higher 2 R value showed a good correlation between the experimental and ANN predicted values. This proved the robustness of the ANN model to predict similar type of system (COX from other Streptomyces sp.) within the limits of the trained data set. The COX activity was enhanced by 1.71 folds after optimization of the reaction conditions.
Silk fibroin/xanthan composite was investigated as a suitable biomedical material for controlled drug delivery, and blending ratios of silk fibroin and xanthan were optimized by response surface methodology (RSM) and artificial neural network (ANN) approach. A non-linear ANN model was developed to predict the effect of blending ratios, percentage swelling and porosity of composite material on cumulative percentage release. The efficiency of RSM was assessed against ANN and it was found that ANN is better in optimizing and modeling studies for the fabrication of the composite material. In-vitro release studies of the loaded drug chloramphenicol showed that the optimum composite scaffold was able to minimize burst release of drug and was followed by controlled release for 5 days. Mechanistic study of release revealed that the drug release process is diffusion controlled. Moreover, during tissue engineering application, investigation of release pattern of incorporated bioactive agent is beneficial to predict, control and monitor cellular response of growing tissues. This work also presented a novel insight into usage of various drug release model to predict material properties. Based on the goodness of fit of the model, Korsmeyer-Peppas was found to agree well with experimental drug release profile, which indicated that the fabricated material has swellable nature. The chloramphenicol (CHL) loaded scaffold showed better efficacy against gram positive and gram negative bacteria. CHL loaded SFX55 (50:50) scaffold shows promising biocomposite for drug delivery and tissue engineering applications.
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